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Computing with Memristor Networks

It has been suggested that CMOS technologies will hit scaling limits due to fundamental
design issues at the regime of molecular electronics. In this project, the memristor device
has been evaluated as a candidate for building high-density, high-performance computers
at such a scale. Although memristors are already under active research and development
as random access memory, in this project, we evaluate their potential for neuromorphic
(brain-inspired) information processing in the context of reservoir computing. We quantify
a memristor network's capability to analyze sets of time-dependent input data for
pattern recognition applications. We pose the following key question: given a network of
a certain design, which signals might it be particularly adept at recognizing? To answer
that question, a rigorous mathematical approach has been developed and implemented
as computer software. Our preliminary results indicate that the conceptual approach
that has been developed can be used to answer this question, and suggest that memristor
networks are capable of real-time pattern recognition.

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BibTeX @mastersthesis{Bennett2014,author={Bennett, Christopher H. and Konkoli, Zoran},title={Computing with Memristor Networks},abstract={It has been suggested that CMOS technologies will hit scaling limits due to fundamental
design issues at the regime of molecular electronics. In this project, the memristor device
has been evaluated as a candidate for building high-density, high-performance computers
at such a scale. Although memristors are already under active research and development
as random access memory, in this project, we evaluate their potential for neuromorphic
(brain-inspired) information processing in the context of reservoir computing. We quantify
a memristor network's capability to analyze sets of time-dependent input data for
pattern recognition applications. We pose the following key question: given a network of
a certain design, which signals might it be particularly adept at recognizing? To answer
that question, a rigorous mathematical approach has been developed and implemented
as computer software. Our preliminary results indicate that the conceptual approach
that has been developed can be used to answer this question, and suggest that memristor
networks are capable of real-time pattern recognition.},publisher={Extern, UPL-instans},place={Göteborg},year={2014},keywords={memristor devices, nanoelectronics, molecular electronics, neuromorphic},note={48},}

RefWorks RT GenericSR PrintID 218659A1 Bennett, Christopher H.A1 Konkoli, ZoranT1 Computing with Memristor NetworksYR 2014AB It has been suggested that CMOS technologies will hit scaling limits due to fundamental
design issues at the regime of molecular electronics. In this project, the memristor device
has been evaluated as a candidate for building high-density, high-performance computers
at such a scale. Although memristors are already under active research and development
as random access memory, in this project, we evaluate their potential for neuromorphic
(brain-inspired) information processing in the context of reservoir computing. We quantify
a memristor network's capability to analyze sets of time-dependent input data for
pattern recognition applications. We pose the following key question: given a network of
a certain design, which signals might it be particularly adept at recognizing? To answer
that question, a rigorous mathematical approach has been developed and implemented
as computer software. Our preliminary results indicate that the conceptual approach
that has been developed can be used to answer this question, and suggest that memristor
networks are capable of real-time pattern recognition.PB Extern, UPL-instans,PB Institutionen för mikroteknologi och nanovetenskap, Bionanosystem, Chalmers tekniska högskola,LA engOL 30